D3GF: A Study on Optimal Defense Performance Evaluation of Drone-Type Moving Target Defense Through Game Theory

Based on the paradigm shift in modern warfare, ground forces conduct pilot operations of wireless unmanned maneuvering systems, such as tactical drones, in the form of manned and unmanned cooperative tactics after deploying the relevant systems in the battlefield. However, security considerations for relevant systems are limited to the scope of using only the existing end-to-end encryption and public key authentication modules, and no defense strategy to actively respond to specialized cyber-electronic warfare threats has been officially established. To drastically reduce both the potential attack surface and security vulnerabilities of drones employed in network-centric-warfare, a proactive defense technology that expires the effectiveness of attacks by avoiding invasive action at the target is expected to be essential. Accordingly, this paper proposes the concept of active moving-target-defense (MTD), an element of cyber deception that minimizes the rate of success of cyber-attacks while conversely maximizing both defense predominance and attack complexity asymmetrically, exclusive according to partially observable Markov decision process (POMDP)-based threat modeling that considers both the internal and external operation sequences of target drones. To optimally design the proposed drone-type MTD based on the Pareto frontier, we additionally advanced and simulated a drone-based defensive deception game framework (D3GF), which represents a general-sum combat framework reflecting decision logics such as the perfect Bayesian Nash equilibrium, stochastic Stackelberg, and partial signal game. This study was conducted to compare and calculate the efficiencies of the drone-type MTD's deceptive defense, which had not been considered in prior studies, by unique environmental features inside and outside the drone. Furthermore, we conducted a detailed performance evaluation considering game metrics based on sensitivity analysis. Hereafter, the drone-type MTD will be extended into an actual active drone protection technology combining cyber flare-type avoidance strategies and cyber camouflage-type disarrangement strategies by expanding its optimization domain as a hypergame, while integrating it with drone decoy elements.

Automated summary

Based on the paradigm shift in modern warfare, this research proposes the concept of active moving-target-defense (MTD), which minimizes the rate of success of cyber-attacks on drones by considering both internal and external operation sequences. A drone-based defensive deception game framework (D3GF) was developed and simulated to optimally design the proposed drone-type MTD. The study aims to compare the efficiencies of deceptive defense in unique environmental features and provide a detailed performance evaluation.